GraphFAS: A Distributed System for Automated Graph Feature Generation and Selection in Industrial Transaction Networks

2026-09-08

AI Summary

为解决工业交易网络中欺诈检测的特征生成和选择问题，提出GraphFAS系统，通过非参数图特征生成模块和自动分布式特征选择算法，提高效率与性能。

Abstract

Industrial fraud detection often relies on costly expert-crafted features that overlook graph-structured relational signals, while GNNs often do not meet the interpretability and deployment requirements of financial risk control. We propose GraphFAS (Graph Feature Automated Selection), a distributed feature selection procedure based on Boruta that bridges this gap through: (1) a non-parametric graph feature generation module that constructs explicit, interpretable structural features via multi-hop subgraph extraction and multi-scale aggregation without learned parameters; and (2) an automated distributed feature selection algorithm extending Boruta with median-based aggregation across partitions to robustly identify informative features at scale with minimal domain expertise. Compared with end-to-end GNN pipelines, GraphFAS decouples feature aggregation from model training, enabling direct integration with tabular models and direct compatibility with TreeSHAPbased explanations. Deployed in Alipay, GraphFAS delivers orderof-magnitude improvements in engineering efficiency while showing strong performance against expert-driven and graph-learning baselines on large-scale graphs.

Problem

• Industrial fraud detection
• graph-structured relational signals
• interpretability and deployment requirements
• financial risk control

Innovation

• non-parametric graph feature generation
• automated distributed feature selection
• Boruta extension
• multi-hop subgraph extraction
• multi-scale aggregation